Laser interstitial thermal therapy for recurrent glioblastomas: a systematic review and meta-analysis.

We aim to investigate the efficacy and safety of laser interstitial thermal therapy (LITT) in treating recurrent glioblastomas (rGBMs). A comprehensive search was conducted in four databases to identify studies published between January 2001 and June 2022 that reported prognosis information of rGBM patients treated with LITT as the primary therapy. The primary outcomes of interest were progression-free survival (PFS) and overall survival (OS) at 6 and 12 months after LITT intervention. Adverse events and complications were also evaluated. Eight eligible non-comparative studies comprising 128 patients were included in the analysis. Seven studies involving 120 patients provided data for the analysis of PFS. The pooled PFS rate at 6 months after LITT was 25% (95% CI 15-37%, I2 = 53%), and at 12 months, it was 9% (95% CI 4-15%, I2 = 24%). OS analysis was performed on 54 patients from six studies, with an OS rate of 92% (95% CI 84-100%, I2 = 0%) at 6 months and 42% (95% CI 13-73%, I2 = 67%) at 12 months after LITT. LITT demonstrates a favorable safety profile with low complication rates and promising tumor control and overall survival rates in patients with rGBMs. Tumor volume and performance status are important factors that may influence the effectiveness of LITT in selected patients. Additionally, the combination of LITT with immune-based therapy holds promise. Further well-designed clinical trials are needed to expand the application of LITT in glioma treatment.

Subventricular zone-associated classification in isocitrate dehydrogenase-wildtype glioblastomas: improved prognostic value through integration of FLAIR with contrast-enhanced imaging.

OBJECTIVE: Controversy surrounds the prognostic value of contrast-enhanced T1-weighted (T1CE) imaging-based subventricular zone (SVZ) classification in isocitrate dehydrogenase (IDH)-wildtype glioblastomas (GBMs). In this study, the authors aimed to assess the potential of incorporating FLAIR imaging into T1CE imaging-based classification for improving prognostic accuracy. METHODS: A retrospective analysis was conducted on 281 patients with IDH-wildtype GBM. T1CE imaging-based classification was performed, and T2-weighted/FLAIR imaging was integrated to evaluate its prognostic estimation ability. Based on the relationship between the tumors and SVZ, patients were categorized into SVZ+ and SVZ- cohorts based on T1CE and T2-weighted/FLAIR imaging findings. Kaplan-Meier and Cox proportional hazards regression analyses were used to assess progression-free survival (PFS) and overall survival (OS), respectively. Patients were then categorized into three subgroups based on their combined classifications: group 1 (SVZ+ on T1CE and T2-weighted/FLAIR imaging), group 2 (SVZ- on T1CE but SVZ+ on T2-weighted/FLAIR imaging), and group 3 (SVZ- on T1CE and T2-weighted/FLAIR imaging). Subgroup analysis was used to evaluate differences in clinical and molecular factors as well as in prognoses. RESULTS: The T1CE imaging-based classification failed to stratify OS between SVZ+ and SVZ- cohorts (16.0 vs 20.0 months, p = 0.36). Survival analysis revealed similar prognoses for patients in groups 1 and 2, and patients in group 2 exhibited worse OS compared with those in group 3 (19.0 vs 23.5 months, p = 0.024). Logistic regression identified lower Karnofsky Performance Status (KPS) (p = 0.011), tumor diameter (p = 0.002), and telomerase reverse transcriptase (TERT) promoter mutation (p = 0.003) to be associated with a higher incidence of group 2 GBMs. Additionally, T2-weighted/FLAIR imaging-based classification provided significant prognostic value (17.0 vs 23.5 months p = 0.021) and was found to be an independent prognostic factor in the Cox multivariate analysis (HR 1.79, 95% CI 1.08-2.96; p = 0.024). CONCLUSIONS: This study underscores the limitations of T1CE imaging-based SVZ-associated classification in predicting prognosis for IDH-wildtype GBMs. The authors therefore propose an integrated approach that involves T2-weighted/FLAIR imaging that can provide improved prognostic ability. Notably, the presence of TERT promoter mutation was identified as a critical factor in nonenhancing tumor infiltration into the SVZ. Further validation through extensive cohort studies is recommended to confirm these findings.

Prediction of IDH mutation status of glioma based on terahertz spectral data.

Gliomas are the most common type of primary tumor in the central nervous system in adults. Isocitrate dehydrogenase (IDH) mutation status is an important molecular biomarker for adult diffuse gliomas. In this study, we were aiming to predict IDH mutation status based on terahertz time-domain spectroscopy technology. Ninety-two frozen sections of glioma tissue from nine patients were included, and terahertz spectroscopy data were obtained. Through Least Absolute Shrinkage and Selection Operator (LASSO), Principal component analysis (PCA), and Random forest (RF) algorithms, a predictive model for predicting IDH mutation status in gliomas was established based on the terahertz spectroscopy dataset with an AUC of 0.844. These results indicate that gliomas with different IDH mutation status have different terahertz spectral features, and the use of terahertz spectroscopy can establish a predictive model of IDH mutation status, providing a new way for glioma research.

Hybrid Prediction Method for ECG Signals Based on VMD, PSR, and RBF Neural Network.

To explore a method to predict ECG signals in body area networks (BANs), we propose a hybrid prediction method for ECG signals in this paper. The proposed method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network to predict an ECG signal. To reduce the nonstationarity and randomness of the ECG signal, we use VMD to decompose the ECG signal into several intrinsic mode functions (IMFs) with finite bandwidth, which is helpful to improve the prediction accuracy. The input parameters of the RBF neural network affect the prediction accuracy and computational burden. We employ PSR to optimize input parameters of the RBF neural network. To evaluate the prediction performance of the proposed method, we carry out many simulation experiments on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the root mean square error (RMSE) and mean absolute error (MAE) of the proposed method are of 10-3 magnitude, while the RMSE and MAE of some competitive prediction methods are of 10-2 magnitude. Compared with other several prediction methods, our method obviously improves the prediction accuracy of ECG signals.